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Mind Map: Polynomials | Mathematics (Maths) Class 10

The document Mind Map: Polynomials | Mathematics (Maths) Class 10 is a part of the Class 10 Course Mathematics (Maths) Class 10.
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FAQs on Mind Map: Polynomials - Mathematics (Maths) Class 10

1. What are polynomials and how are they classified?
Ans. Polynomials are algebraic expressions consisting of variables raised to non-negative integer powers, combined using addition, subtraction, and multiplication. They are classified based on their degree (the highest power of the variable) and the number of terms. For example, a polynomial of degree 2 with three terms is called a quadratic trinomial.
2. How do you add and subtract polynomials?
Ans. To add or subtract polynomials, you combine like terms, which are terms that have the same variable raised to the same power. For example, to add \(2x^2 + 3x + 4\) and \(x^2 + 2x + 1\), you combine \(2x^2\) and \(x^2\) to get \(3x^2\), \(3x\) and \(2x\) to get \(5x\), and \(4\) and \(1\) to get \(5\). The result is \(3x^2 + 5x + 5\).
3. What is the difference between a monomial, binomial, and trinomial?
Ans. A monomial is an algebraic expression with only one term, such as \(5x\) or \(3x^2\). A binomial has two terms, like \(x + 3\) or \(2x^2 - 4\). A trinomial contains three terms, such as \(x^2 + 5x + 6\) or \(3x^3 - x + 2\). The classification is based on the number of terms in the expression.
4. How do you multiply polynomials?
Ans. To multiply polynomials, you apply the distributive property, multiplying each term in the first polynomial by each term in the second polynomial. For instance, to multiply \((x + 2)\) by \((x + 3)\), you would calculate \(x^2 + 3x + 2x + 6\), which simplifies to \(x^2 + 5x + 6\).
5. What are the common uses of polynomials in real life?
Ans. Polynomials are used in various fields such as physics for modeling motion, in finance for calculating compound interest, and in engineering for designing structures. They also appear in computer graphics for rendering curves and surfaces, and in statistics for regression analysis, allowing for predictions based on data trends.
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